Currently, automated information retrieval systems predominantly use either chat-bot techniques or enterprise search systems for processing and executing user queries. Additionally, both chat-bot techniques and enterprise search systems are being used for increasingly disparate needs for retrieval of information. Unfortunately, both also suffer from major limitations and drawbacks which make them cumbersome and inefficient to be used in various real life information retrieval situations.
With respect to chat-bot techniques, typically these techniques process a query from a user to find out a context and then engage the user in a dialogue to get a better understanding of the context (when context is not clear) for retrieving information. Unfortunately, these chat-bot techniques are designed to work with a single context and rely on this dialogue to receive further clarifying inputs from user when the context is not clear. As a result, these techniques are ineffective for retrieving information when a user is interested in multiple contexts and/or when a user does not have the patience, inclination and/or time to answer clarifying questions.
With respect to enterprise search systems, these systems treat each query from a user in isolation and does not maintain any context between queries. As a result, with each subsequent query the user is required to add a condition to the query and the query becomes longer and needs to be rewritten. This is different when compared to asking questions to another human being because human beings can maintain context among the series of queries being asked.
A simple example of this is illustrated with the following example and assumes all data is available to the enterprise search system and the human beings involved with the following sequential queries:    Query 1: How many customers in London?    System response: Number of customers in London.    Human being response: Number of customers in London.    Query 2: How many with age less than 35?    System response: Question not clear.    Human being response: Number of customers in London with age less than 35.Accordingly, as illustrated in the example above the enterprise search system treats each of the queries, i.e. Query 1 and Query 2, in isolation and as a result is unable to effectively retrieve the requested information in Query 2 without Query 2 being rewritten to include the prior condition from Query 1.
Another problem with chat-bot techniques and enterprise search systems for processing and executing user queries is neither effectively utilizes an enterprise profile of the user in the retrieval of the information. Instead, an enterprise profile of a user is merely used for authorizing viewing of the retrieved information and provides no ranking or short-listing of results based on what is more relevant for the user based on the enterprise profile.